The Usefulness of the APACHE II Score in Obstetric Critical Care: A Structured Review
Bibliographic record
Abstract
OBJECTIVE: To assess the performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) mortality prediction model in pregnant and recently pregnant women receiving critical care in low-, middle-, and high-income countries during the study period (1985-2015), using a structured literature review. DATA SOURCES: Ovid MEDLINE, Embase, Web of Science, and Evidence-Based Medicine Reviews, searched for articles published between 1985 and 2015. STUDY SELECTION: Twenty-five studies (24 publications), of which two were prospective, were included in the analyses. Ten studies were from high-income countries (HICs), and 15 were from low- and middle-income countries (LMICs). Median study duration and size were six years and 124 women, respectively. DATA SYNTHESIS: ICU admission complicates 0.48% of deliveries, and pregnant and recently pregnant women account for 1.49% of ICU admissions. One quarter were admitted while pregnant, three quarters of these for an obstetric indication and for a median of three days. The median APACHE II score was 10.9, with a median APACHE II-predicted mortality of 16.6%. Observed mortality was 4.6%, and the median standardized mortality ratio was 0.36 (interquartile range 0.23 to 0.73). The standardized mortality ratio was < 0.9 in 24 of 25 studies. Women in HICs were more frequently admitted with a medical comorbidity but were less likely to die than were women in LMICs. CONCLUSION: The APACHE II score consistently overestimates mortality risks for pregnant and recently pregnant women receiving critical care, whether they reside in HICs or LMICs. There is a need for a pregnancy-specific outcome prediction model for these women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".